Visual Anomaly Detection in Production Plants

Alexander Maier, Tim Tack, Oliver Niggemann

2012

Abstract

This paper presents a novel method for visual anomaly detection in production plants. Since the complexity of the plants and the number of signals that have to be monitored by the operator grows, there is a need of tools to overcome the information overflow. The human is highly able to recognize irregularities in figures. More than 80% of the perceived information is captured visually. The approach proposed in this paper exploits this fact and subjects data to make the operator able to find anomalies in the displayed figures. In three steps the operator is lead from the visualization of the normal behavior over the anomaly detection and the localization of the faulty module to the anomalous signal.

Paper Citation

in Harvard Style

Maier A., Tack T. and Niggemann O. (2012). Visual Anomaly Detection in Production Plants . In Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-8565-21-1, pages 67-75. DOI: 10.5220/0004039600670075

in Bibtex Style

@conference{icinco12,
author={Alexander Maier and Tim Tack and Oliver Niggemann},
title={Visual Anomaly Detection in Production Plants},
booktitle={Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2012},
pages={67-75},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004039600670075},
isbn={978-989-8565-21-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Visual Anomaly Detection in Production Plants
SN - 978-989-8565-21-1
AU - Maier A.
AU - Tack T.
AU - Niggemann O.
PY - 2012
SP - 67
EP - 75
DO - 10.5220/0004039600670075